Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix

To improve the efficiency of blur kernel estimation based on prior knowledge, a method of deblurring an image based on rich edge region extraction using a gray-level co-occurrence matrix is proposed in this paper. First, the relationship between the image edge information and the related coefficients of a gray-level co-occurrence matrix is analyzed, based on which an index representing the amount of image edge information is proposed. Next, high-frequency layer information is extracted from the blurred image to be processed with a bilinear interpolation method in the luminance channel. Subsequently, the high-frequency layer image is divided into nine regions, based on a sliding window, and the rich edge region index of each region is calculated; then, the region with the richest edge information is extracted. Finally, the extracted rich edge region, instead of the entire motion blurred image, is used to estimate the blur kernel with L0-regularized intensity and gradient prior, and the blurred image is blindly restored. An image quality evaluation function and the operation time are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can improve the recovery efficiency while ensuring the recovery quality as well.